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pyspark-bbn is a scalable, massively parallel processing MPP framework for learning structures and parameters of Bayesian Belief Networks BBNs. The API leverages Apache Spark for high-performance HPC computing needs. While there are papers and approaches to scalable learning of BBNs, to our knowledge, pyspark-bbn is the first of its kind to leverage Apache Spark. We have implemented several structure learning algorithms for both discrete and gaussian variables.

In combination with the power of py-bbn for exact inference, pyspark-bbn is an effective and scalable end-to-end solution for causal learning and inference using BBNs. Please contact us at to discuss our products and services around py-bbn and pyspark-bbn. In the meanwhile, please enjoy reading the documentation of pyspark-bbn and observe the elegance, simplicity and power of The Art of the Possible with the API.


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author={One-Off Coder},


Jee Vang, Ph.D.